A logistics company records the exact time each delivery truck departs and arrives. They want to analyze the duration of trips to identify factors that affect delivery times. Which characteristic of this data makes it more suitable for survival analysis than for standard time series analysis?

The data is collected at regular intervals over time

The data shows a clear trend and seasonality

The data consists of time-to-event observations with potential censoring

The data is measured on a continuous scale

A data scientist is analyzing the effect of a marketing campaign on weekly sales. They have sales data for 52 weeks before and 26 weeks after the campaign. The campaign was launched at a specific time, and sales show a clear upward trend even before the campaign. Which approach is most appropriate to estimate the causal effect of the campaign?

Interrupted time series analysis with a control series

Survival analysis of time until sales exceed a threshold

Granger causality test between campaign indicators and sales

Simple before-and-after comparison of average sales

What is the primary goal of causal inference?

To describe the relationship between two variables

To determine whether a change in one variable causes a change in another

To summarize the central tendency of a dataset

To predict future outcomes based on historical data

Which technique is commonly used to decompose a time series into its trend, seasonal, and residual components?

Autoregressive integrated moving average (ARIMA)

Seasonal decomposition of time series (STL)

Why is causal inference important in statistical analysis?

It simplifies data visualization

It helps identify which variables are correlated

It enables effective decision-making by understanding the impact of interventions

It allows for accurate prediction of future values

CompTIADataX

Domain 1Objective 4

Temporal Models DY0-001 Practice Questions (Page 1)

Part of the Mathematics and statistics domain, which accounts for 17% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~11–19 in this domain), expect 3–5 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

9concepts

17%of the exam

Questions 1–5

1application · medium
A logistics company records the exact time each delivery truck departs and arrives. They want to analyze the duration of trips to identify factors that affect delivery times. Which characteristic of this data makes it more suitable for survival analysis than for standard time series analysis?
Select an answer first
2expert · hard
A data scientist is analyzing the effect of a marketing campaign on weekly sales. They have sales data for 52 weeks before and 26 weeks after the campaign. The campaign was launched at a specific time, and sales show a clear upward trend even before the campaign. Which approach is most appropriate to estimate the causal effect of the campaign?
Select an answer first
3foundation · easy
What is the primary goal of causal inference?
Select an answer first
4foundation · easy
Which technique is commonly used to decompose a time series into its trend, seasonal, and residual components?
Select an answer first
5foundation · easy
Why is causal inference important in statistical analysis?
Select an answer first

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